ControlAR

Controllable Image Generation with Autoregressive Models

Zongming Li^1,\*, [Tianheng Cheng](https://scholar.google.com/citations?user=PH8rJHYAAAAJ&hl=zh-CN)^1,\*, Shoufa Chen², Peize Sun², Haocheng Shen³,Longjin Ran³, Xiaoxin Chen³, [Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu)¹, [Xinggang Wang](https://xwcv.github.io/)^{1,📧 1} Huazhong University of Science and Technology, ² The University of Hong Kong ³ vivo AI Lab (\* equal contribution, 📧 corresponding author) [](https://arxiv.org/abs/2410.02705)

News

[2024-10-04]: We have released the technical report of ControlAR. Code, models, and demos are coming soon!

Highlights

• ControlAR explores an effective yet simple conditional decoding strategy for adding spatial controls to autoregressive models, e.g., LlamaGen, from a sequence perspective.

• ControlAR supports arbitrary-resolution image generation with autoregressive models without hand-crafted special tokens or resolution-aware prompts.

Results

We provide both quantitative and qualitative comparisons with diffusion-based methods in the technical report!

Acknowledgments

The development of ControlAR is based on LlamaGen, ControlNet, ControlNet++, and AiM, and we sincerely thank the contributors for thoese great works!

Citation

If you find ControlAR is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

@article{li2024controlar,
      title={ControlAR: Controllable Image Generation with Autoregressive Models}, 
      author={Zongming Li, Tianheng Cheng, Shoufa Chen, Peize Sun, Haocheng Shen, Longjin Ran, Xiaoxin Chen, Wenyu Liu, Xinggang Wang},
      year={2024},
      eprint={2410.02705},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.02705}, 
}